Triple
T35492501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinto metropolitan area |
E1025766
|
entity |
| Predicate | hasCommutingPattern |
P8986
|
FINISHED |
| Object | daily commuting to Pinto |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: daily commuting to Pinto | Statement: [Pinto metropolitan area, hasCommutingPattern, daily commuting to Pinto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommutingPattern Context triple: [Pinto metropolitan area, hasCommutingPattern, daily commuting to Pinto]
-
A.
hasCommuterPattern
chosen
Indicates that there is a characteristic or recurring pattern in how an entity regularly travels between locations, typically for work or daily activities.
-
B.
hasCommuterOrientation
Indicates that an entity is designed or intended primarily for use by commuters, emphasizing suitability for regular travel between home and work or study.
-
C.
commutesBetween
Indicates a regular pattern of travel back and forth between two locations, typically for work, study, or routine activities.
-
D.
hasCommuterTraffic
Indicates that there is regular, recurring traffic flow associated with people traveling between their homes and places of work or study.
-
E.
travelPattern
Indicates the typical routes, frequencies, and behaviors associated with how an entity moves or travels between locations.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76dfbcdd881908c7b0b6bc502252b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a04d8348190a4819666eab42c9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:04 p.m.